Spillover detection for donor selection in synthetic control models
Synthetic control (SC) models are widely used to estimate causal effects in settings with observational time-series data. To identify the causal effect on a target unit, SC requires the existence of additional units that are not impacted by the intervention. Given one of these potential donor units,...
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| Published in: | Journal of causal inference Vol. 13; no. 1; pp. 2 - 5 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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De Gruyter
08.10.2025
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| ISSN: | 2193-3685, 2193-3685 |
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| Abstract | Synthetic control (SC) models are widely used to estimate causal effects in settings with observational time-series data. To identify the causal effect on a target unit, SC requires the existence of additional units that are not impacted by the intervention. Given one of these potential donor units, how can we decide whether it is in fact a
donor – that is, one not subject to spillover effects from the intervention? Such a decision typically requires appealing to strong
domain knowledge specifying the units, which becomes infeasible in situations with large pools of potential donors. In this article, we introduce a practical, theoretically grounded donor selection procedure, aiming to weaken this domain knowledge requirement. Our main result is a theorem that yields the assumptions required to identify donor values at post-intervention time points using only pre-intervention data. We show how this theorem – and the assumptions underpinning it – can be turned into a practical method for detecting potential spillover effects and excluding invalid donors when constructing SCs. Importantly, we employ sensitivity analysis to formally bound the bias in our SC causal estimate in situations where an excluded donor was indeed valid, or where a selected donor was invalid. Using ideas from the proximal causal inference and instrumental variables literature, we show that the excluded donors can nevertheless be leveraged to further debias causal effect estimates. Finally, we illustrate our donor selection procedure on both simulated and real-world datasets. |
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| AbstractList | Synthetic control (SC) models are widely used to estimate causal effects in settings with observational time-series data. To identify the causal effect on a target unit, SC requires the existence of additional units that are not impacted by the intervention. Given one of these potential donor units, how can we decide whether it is in fact a
donor – that is, one not subject to spillover effects from the intervention? Such a decision typically requires appealing to strong
domain knowledge specifying the units, which becomes infeasible in situations with large pools of potential donors. In this article, we introduce a practical, theoretically grounded donor selection procedure, aiming to weaken this domain knowledge requirement. Our main result is a theorem that yields the assumptions required to identify donor values at post-intervention time points using only pre-intervention data. We show how this theorem – and the assumptions underpinning it – can be turned into a practical method for detecting potential spillover effects and excluding invalid donors when constructing SCs. Importantly, we employ sensitivity analysis to formally bound the bias in our SC causal estimate in situations where an excluded donor was indeed valid, or where a selected donor was invalid. Using ideas from the proximal causal inference and instrumental variables literature, we show that the excluded donors can nevertheless be leveraged to further debias causal effect estimates. Finally, we illustrate our donor selection procedure on both simulated and real-world datasets. Synthetic control (SC) models are widely used to estimate causal effects in settings with observational time-series data. To identify the causal effect on a target unit, SC requires the existence of additional units that are not impacted by the intervention. Given one of these potential donor units, how can we decide whether it is in fact a valid donor – that is, one not subject to spillover effects from the intervention? Such a decision typically requires appealing to strong a priori domain knowledge specifying the units, which becomes infeasible in situations with large pools of potential donors. In this article, we introduce a practical, theoretically grounded donor selection procedure, aiming to weaken this domain knowledge requirement. Our main result is a theorem that yields the assumptions required to identify donor values at post-intervention time points using only pre-intervention data. We show how this theorem – and the assumptions underpinning it – can be turned into a practical method for detecting potential spillover effects and excluding invalid donors when constructing SCs. Importantly, we employ sensitivity analysis to formally bound the bias in our SC causal estimate in situations where an excluded donor was indeed valid, or where a selected donor was invalid. Using ideas from the proximal causal inference and instrumental variables literature, we show that the excluded donors can nevertheless be leveraged to further debias causal effect estimates. Finally, we illustrate our donor selection procedure on both simulated and real-world datasets. |
| Author | O’Riordan, Michael Gilligan-Lee, Ciarán M. |
| Author_xml | – sequence: 1 givenname: Michael surname: O’Riordan fullname: O’Riordan, Michael email: moriordan@spotify.com organization: Spotify, London, UK – sequence: 2 givenname: Ciarán M. surname: Gilligan-Lee fullname: Gilligan-Lee, Ciarán M. email: ciaran.lee@ucl.ac.uk organization: Spotify, Dublin, Ireland |
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| Cites_doi | https://doi.org/10.1111/j.1467-9868.2011.01016.x 10.1515/jci-2016-0013 10.1214/14-AOAS788 10.1257/jep.31.2.3 10.1017/CBO9780511790942 10.1093/biomet/asy038 10.1146/annurev-statistics-100421-044639 10.1257/000282803321455188 10.1080/01621459.1988.10478694 10.1101/2020.09.21.20198762 10.1111/rssb.12348 10.1002/hec.3258 10.1145/3580305.3599778 10.1111/ajps.12116 10.1038/s41467-020-17419-7 10.1257/000282803321946921 10.1007/978-3-031-16452-1_57 10.1609/aaai.v34i04.5789 10.1111/j.1467-9868.2011.01016.x 10.1080/01621459.2021.1920957 10.1111/j.2517-6161.1983.tb01242.x 10.1016/S0262-4079(20)30817-4 10.1198/jasa.2009.ap08746 10.1017/CBO9780511803161 10.1080/01621459.2021.1929245 10.1093/biomet/ast066 |
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| References | Chernozhukov, V; Wüthrich, K; Zhu, Y. (j_jci-2024-0036_ref_038) 2021; 116 Cinelli, C; Hazlett, C. (j_jci-2024-0036_ref_031) 2020; 82 Nazaret, A; Shi, C; Blei, DM (j_jci-2024-0036_ref_033) 2023 Perov, Y; Graham, L; Gourgoulias, K; Richens, J; Lee, C; Baker, A (j_jci-2024-0036_ref_005) 2020 Miao, W; Shi, X; Tchetgen, ET (j_jci-2024-0036_ref_019) 2020 Mitchell, TJ; Beauchamp, JJ (j_jci-2024-0036_ref_044) 1988; 83 Jeunen, O; Gilligan-Lee, CM; Mehrotra, R; Lalmas, M. (j_jci-2024-0036_ref_008) 2022 Reynaud, H; Vlontzos, A; Dombrowski, M; Lee, C; Beqiri, A; Leeson, P (j_jci-2024-0036_ref_009) 2022 Imbens, GW (j_jci-2024-0036_ref_029) 2003; 93 Brodersen, KH; Gallusser, F; Koehler, J; Remy, N; Scott, SL (j_jci-2024-0036_ref_014) 2015; 9 Van Goffrier, G; Maystre, L; Gilligan-Lee, C. (j_jci-2024-0036_ref_010) 2023 Abadie, A; Gardeazabal, J. (j_jci-2024-0036_ref_011) 2003; 93 Kuroki, M; Pearl, J. (j_jci-2024-0036_ref_022) 2014; 101 Shi, X; Miao, W; Tchetgen, ET (j_jci-2024-0036_ref_034) 2022 Rasines, DG; Young, GA (j_jci-2024-0036_ref_041) 2020 Rosenbaum, PR; Rubin, DB (j_jci-2024-0036_ref_028) 1983; 45 Lin, S; Xu, M; Zhang, X; Chao, SK; Huang, YK; Shi, X. (j_jci-2024-0036_ref_017) 2023 Shpitser, I; Wood-Doughty, Z; Tchetgen, EJT. (j_jci-2024-0036_ref_026) 2021 Lee, CM; Spekkens, RW (j_jci-2024-0036_ref_001) 2017; 5 Abadie, A; Diamond, A; Hainmueller, J. (j_jci-2024-0036_ref_012) 2010; 105 Kreif, N; Grieve, R; Hangartner, D; Turner, AJ; Nikolova, S; Sutton, M. (j_jci-2024-0036_ref_015) 2016; 25 Ben-Michael, E; Feller, A; Rothstein, J. (j_jci-2024-0036_ref_037) 2021; 116 Imbens, G; Kallus, N; Mao, X; Wang, Y. (j_jci-2024-0036_ref_025) 2022 Abadie, A; Diamond, A; Hainmueller, J. (j_jci-2024-0036_ref_013) 2015; 59 Yekutieli, D. (j_jci-2024-0036_ref_040) 2012 02; 74 Gilligan-Lee, CM; Hart, C; Richens, J; Johri, S. (j_jci-2024-0036_ref_007) 2022 Veitch, V; Zaveri, A. (j_jci-2024-0036_ref_030) 2020; 33 Shi, X; Li, K; Miao, W; Hu, M; Tchetgen, ET (j_jci-2024-0036_ref_020) 2023 Richens, JG; Lee, CM; Johri, S. (j_jci-2024-0036_ref_003) 2020; 11 Athey, S; Imbens, GW (j_jci-2024-0036_ref_016) 2017; 31 Miao, W; Geng, Z; Tchetgen Tchetgen, EJ (j_jci-2024-0036_ref_023) 2018; 105 Gilligan-Lee, C. (j_jci-2024-0036_ref_002) 2020; 246 Kuchibhotla, AK; Kolassa, JE; Kuffner, TA (j_jci-2024-0036_ref_042) 2022; 9 Tchetgen, EJT; Ying, A; Cui, Y; Shi, X; Miao, W. (j_jci-2024-0036_ref_024) 2020 2025100816581130426_j_jci-2024-0036_ref_011 2025100816581130426_j_jci-2024-0036_ref_033 2025100816581130426_j_jci-2024-0036_ref_010 2025100816581130426_j_jci-2024-0036_ref_032 2025100816581130426_j_jci-2024-0036_ref_031 2025100816581130426_j_jci-2024-0036_ref_030 2025100816581130426_j_jci-2024-0036_ref_019 2025100816581130426_j_jci-2024-0036_ref_018 2025100816581130426_j_jci-2024-0036_ref_017 2025100816581130426_j_jci-2024-0036_ref_039 2025100816581130426_j_jci-2024-0036_ref_016 2025100816581130426_j_jci-2024-0036_ref_038 2025100816581130426_j_jci-2024-0036_ref_015 2025100816581130426_j_jci-2024-0036_ref_037 2025100816581130426_j_jci-2024-0036_ref_014 2025100816581130426_j_jci-2024-0036_ref_036 2025100816581130426_j_jci-2024-0036_ref_013 2025100816581130426_j_jci-2024-0036_ref_035 2025100816581130426_j_jci-2024-0036_ref_012 2025100816581130426_j_jci-2024-0036_ref_034 2025100816581130426_j_jci-2024-0036_ref_022 2025100816581130426_j_jci-2024-0036_ref_044 2025100816581130426_j_jci-2024-0036_ref_021 2025100816581130426_j_jci-2024-0036_ref_043 2025100816581130426_j_jci-2024-0036_ref_020 2025100816581130426_j_jci-2024-0036_ref_042 2025100816581130426_j_jci-2024-0036_ref_041 2025100816581130426_j_jci-2024-0036_ref_040 2025100816581130426_j_jci-2024-0036_ref_009 2025100816581130426_j_jci-2024-0036_ref_008 2025100816581130426_j_jci-2024-0036_ref_007 2025100816581130426_j_jci-2024-0036_ref_029 2025100816581130426_j_jci-2024-0036_ref_006 2025100816581130426_j_jci-2024-0036_ref_028 2025100816581130426_j_jci-2024-0036_ref_005 2025100816581130426_j_jci-2024-0036_ref_027 2025100816581130426_j_jci-2024-0036_ref_004 2025100816581130426_j_jci-2024-0036_ref_026 2025100816581130426_j_jci-2024-0036_ref_003 2025100816581130426_j_jci-2024-0036_ref_025 2025100816581130426_j_jci-2024-0036_ref_002 2025100816581130426_j_jci-2024-0036_ref_024 2025100816581130426_j_jci-2024-0036_ref_001 2025100816581130426_j_jci-2024-0036_ref_023 |
| References_xml | – volume: 83 start-page: 1023 issue: 404 year: 1988 end-page: 32 ident: j_jci-2024-0036_ref_044 article-title: Bayesian variable selection in linear regression publication-title: J Am Stat Assoc. – year: 2022 ident: j_jci-2024-0036_ref_034 publication-title: A selective review of negative control methods in epidemiology. – volume: 11 start-page: 1 issue: 1 year: 2020 end-page: 9 ident: j_jci-2024-0036_ref_003 article-title: Improving the accuracy of medical diagnosis with causal machine learning publication-title: Nat Commun. – year: 2022 ident: j_jci-2024-0036_ref_009 publication-title: DaARTAGNAN: counterfactual video generation – volume: 45 start-page: 212 issue: 2 year: 1983 end-page: 8 ident: j_jci-2024-0036_ref_028 article-title: Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome publication-title: J R Stat Soc Ser B (Methodological) – volume: 116 start-page: 1789 issue: (536) year: 2021 end-page: 803 ident: j_jci-2024-0036_ref_037 article-title: The augmented synthetic control method publication-title: J Am Stat Assoc. – volume: 59 start-page: 495 issue: 2 year: 2015 end-page: 510 ident: j_jci-2024-0036_ref_013 article-title: Comparative politics and the synthetic control method publication-title: Am J Polit Sci. – volume: 74 issue: (3) year: 2012 02 ident: j_jci-2024-0036_ref_040 article-title: Adjusted Bayesian inference for selected parameters publication-title: J R Stat Soc Ser B Stat Methodol. doi: https://doi.org/10.1111/j.1467-9868.2011.01016.x – year: 2023 ident: j_jci-2024-0036_ref_017 publication-title: Balancing approach for causal inference at scale. – year: 2022 ident: j_jci-2024-0036_ref_008 publication-title: Disentangling causal effects from sets of interventions in the presence of unobserved confounders. – volume: 93 start-page: 113 issue: 1 year: 2003 end-page: 32 ident: j_jci-2024-0036_ref_011 article-title: The economic costs of conflict: A case study of the Basque Country publication-title: Am Econ Rev. – year: 2020 ident: j_jci-2024-0036_ref_019 publication-title: A confounding bridge approach for double negative control inference on causal effects. – volume: 5 start-page: 2 year: 2017 ident: j_jci-2024-0036_ref_001 article-title: Causal inference via algebraic geometry: feasibility tests for functional causal structures with two binary observed variables publication-title: J Causal Inference. – volume: 25 start-page: 1514 issue: 12 year: 2016 end-page: 28 ident: j_jci-2024-0036_ref_015 article-title: Examination of the synthetic control method for evaluating health policies with multiple treated units publication-title: Health Econom. – year: 2023 ident: j_jci-2024-0036_ref_020 publication-title: Theory for identification and inference with synthetic controls: a proximal causal inference framework. – volume: 9 start-page: 247 issue: 1 year: 2015 end-page: 74 ident: j_jci-2024-0036_ref_014 article-title: Inferring causal impact using Bayesian structural time-series models publication-title: An Appl Stat. – year: 2021 ident: j_jci-2024-0036_ref_026 publication-title: The proximal id algorithm. – year: 2022 ident: j_jci-2024-0036_ref_007 article-title: Leveraging directed causal discovery to detect latent common causes in cause-effect Pairs publication-title: IEEE Transactions on Neural Networks and Learning Systems. – volume: 116 start-page: 1849 issue: (536) year: 2021 end-page: 64 ident: j_jci-2024-0036_ref_038 article-title: An exact and robust conformal inference method for counterfactual and synthetic controls publication-title: J Am Stat Assoc. – volume: 101 start-page: 423 issue: 2 year: 2014 end-page: 37 ident: j_jci-2024-0036_ref_022 article-title: Measurement bias and effect restoration in causal inference publication-title: Biometrika. – volume: 9 start-page: 505 year: 2022 end-page: 27 ident: j_jci-2024-0036_ref_042 article-title: Post-selection inference publication-title: An Rev Stat Appl. – volume: 31 start-page: 3 issue: 2 year: 2017 end-page: 32 ident: j_jci-2024-0036_ref_016 article-title: The state of applied econometrics: Causality and policy evaluation publication-title: J Econ Perspectives. – year: 2020 ident: j_jci-2024-0036_ref_024 publication-title: An introduction to proximal causal learning. – volume: 93 start-page: 126 issue: 2 year: 2003 end-page: 32 ident: j_jci-2024-0036_ref_029 article-title: Sensitivity to exogeneity assumptions in program evaluation publication-title: Am Econ Rev. – volume: 82 start-page: 39 issue: 1 year: 2020 end-page: 67 ident: j_jci-2024-0036_ref_031 article-title: Making sense of sensitivity: Extending omitted variable bias publication-title: J R Stat Soc Ser B (Stat Meth). – year: 2023 ident: j_jci-2024-0036_ref_033 publication-title: On the misspecification of linear assumptions in synthetic control. – volume: 33 start-page: 10999 year: 2020 end-page: 1009 ident: j_jci-2024-0036_ref_030 article-title: Sense and sensitivity analysis: Simple post-hoc analysis of bias due to unobserved confounding publication-title: Adv Neural Inform Proces Syst. – year: 2022 ident: j_jci-2024-0036_ref_025 publication-title: Long-term causal inference under persistent confounding via data combination. – volume: 246 start-page: 32 issue: 3279 year: 2020 end-page: 5 ident: j_jci-2024-0036_ref_002 article-title: Causing trouble publication-title: New Sci. – year: 2020 ident: j_jci-2024-0036_ref_041 publication-title: Bayesian selective inference: non-informative priors – volume: 105 start-page: 987 issue: 4 year: 2018 end-page: 93 ident: j_jci-2024-0036_ref_023 article-title: Identifying causal effects with proxy variables of an unmeasured confounder publication-title: Biometrika. – start-page: p. 1 year: 2020 end-page: 36 ident: j_jci-2024-0036_ref_005 article-title: Multiverse: causal reasoning using importance sampling in probabilistic programming publication-title: Symposium on advances in approximate bayesian inference. PMLR – year: 2023 ident: j_jci-2024-0036_ref_010 publication-title: Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding. – volume: 105 start-page: 493 issue: 490 year: 2010 end-page: 505 ident: j_jci-2024-0036_ref_012 article-title: Synthetic control methods for comparative case studies: Estimating the effect of California - tobacco control program publication-title: J Am Stat Assoc – ident: 2025100816581130426_j_jci-2024-0036_ref_001 doi: 10.1515/jci-2016-0013 – ident: 2025100816581130426_j_jci-2024-0036_ref_026 – ident: 2025100816581130426_j_jci-2024-0036_ref_014 doi: 10.1214/14-AOAS788 – ident: 2025100816581130426_j_jci-2024-0036_ref_005 – ident: 2025100816581130426_j_jci-2024-0036_ref_030 – ident: 2025100816581130426_j_jci-2024-0036_ref_020 – ident: 2025100816581130426_j_jci-2024-0036_ref_032 – ident: 2025100816581130426_j_jci-2024-0036_ref_018 – ident: 2025100816581130426_j_jci-2024-0036_ref_016 doi: 10.1257/jep.31.2.3 – ident: 2025100816581130426_j_jci-2024-0036_ref_039 doi: 10.1017/CBO9780511790942 – ident: 2025100816581130426_j_jci-2024-0036_ref_023 doi: 10.1093/biomet/asy038 – ident: 2025100816581130426_j_jci-2024-0036_ref_036 – ident: 2025100816581130426_j_jci-2024-0036_ref_042 doi: 10.1146/annurev-statistics-100421-044639 – ident: 2025100816581130426_j_jci-2024-0036_ref_034 – ident: 2025100816581130426_j_jci-2024-0036_ref_011 doi: 10.1257/000282803321455188 – ident: 2025100816581130426_j_jci-2024-0036_ref_044 doi: 10.1080/01621459.1988.10478694 – ident: 2025100816581130426_j_jci-2024-0036_ref_008 – ident: 2025100816581130426_j_jci-2024-0036_ref_024 doi: 10.1101/2020.09.21.20198762 – ident: 2025100816581130426_j_jci-2024-0036_ref_025 – ident: 2025100816581130426_j_jci-2024-0036_ref_031 doi: 10.1111/rssb.12348 – ident: 2025100816581130426_j_jci-2024-0036_ref_015 doi: 10.1002/hec.3258 – ident: 2025100816581130426_j_jci-2024-0036_ref_027 – ident: 2025100816581130426_j_jci-2024-0036_ref_017 doi: 10.1145/3580305.3599778 – ident: 2025100816581130426_j_jci-2024-0036_ref_006 – ident: 2025100816581130426_j_jci-2024-0036_ref_021 – ident: 2025100816581130426_j_jci-2024-0036_ref_013 doi: 10.1111/ajps.12116 – ident: 2025100816581130426_j_jci-2024-0036_ref_019 – ident: 2025100816581130426_j_jci-2024-0036_ref_003 doi: 10.1038/s41467-020-17419-7 – ident: 2025100816581130426_j_jci-2024-0036_ref_029 doi: 10.1257/000282803321946921 – ident: 2025100816581130426_j_jci-2024-0036_ref_033 – ident: 2025100816581130426_j_jci-2024-0036_ref_009 doi: 10.1007/978-3-031-16452-1_57 – ident: 2025100816581130426_j_jci-2024-0036_ref_004 doi: 10.1609/aaai.v34i04.5789 – ident: 2025100816581130426_j_jci-2024-0036_ref_040 doi: 10.1111/j.1467-9868.2011.01016.x – ident: 2025100816581130426_j_jci-2024-0036_ref_038 doi: 10.1080/01621459.2021.1920957 – ident: 2025100816581130426_j_jci-2024-0036_ref_028 doi: 10.1111/j.2517-6161.1983.tb01242.x – ident: 2025100816581130426_j_jci-2024-0036_ref_010 – ident: 2025100816581130426_j_jci-2024-0036_ref_041 – ident: 2025100816581130426_j_jci-2024-0036_ref_002 doi: 10.1016/S0262-4079(20)30817-4 – ident: 2025100816581130426_j_jci-2024-0036_ref_007 – ident: 2025100816581130426_j_jci-2024-0036_ref_012 doi: 10.1198/jasa.2009.ap08746 – ident: 2025100816581130426_j_jci-2024-0036_ref_035 doi: 10.1017/CBO9780511803161 – ident: 2025100816581130426_j_jci-2024-0036_ref_037 doi: 10.1080/01621459.2021.1929245 – ident: 2025100816581130426_j_jci-2024-0036_ref_043 – ident: 2025100816581130426_j_jci-2024-0036_ref_022 doi: 10.1093/biomet/ast066 |
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| Title | Spillover detection for donor selection in synthetic control models |
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